Reading

I am also interested more broadly in biases in citizen science.

makes me wonder: Are there any studies concerning biases in non-citizen science or is ‘bias in citizen science’ defined as difference in thematic focus or social structure from conventional non-citizen science so non-citizen science is by definition non-biased?

Same could be asked for conventionally gathered geodata - has anyone ever looked systematically at bias in non-crowdsourced geodata collections?

Setting this aside - the general recommendation for scientists studying OSM is to get a decent amount of experience on the project before beginning the study. Your user account has zero edits at the moment - which makes your approach a bit like someone starting a study on a Japanese sociology topic without ever having been to Japan…

Deswegen sagte ich Fernbereich. Die Winkelangaben in OSM beziehen sich üblicherweise auf die Sicht-Einschränkungen in der unmittelbaren Umgebung. Bei weiter entfernten Objekten spielen die Details dann nicht mehr so eine Rolle. Ob auf einem 5 Kilometer entfernten Berg Bäume wachsen oder nicht hat auf die Sichtbarkeit der dahinter liegenden Berge nicht mehr so viel Einfluss.

Nett.

Wenn Du jetzt noch Langeweile hast, könntest Du diese Darstellung der Sichtbarkeit im Nahbereich ergänzen durch eine Berechnung der Sichtbarkeit im Fernbereich auf Basis des Reliefs.

Das geht dann aber nicht mehr so mal eben im Browser…

@Jedrzej - you need to change the color scale (in terms of nodes per square kilometer) as you zoom because the maximum node density per square kilometer is much migher if you integrate over smaller pixels at the high zoom levels than if you integrate over larger pixels.

But the non-linearity of the color scale could be adjusted to approximately maintain the overall color level of the map.

@tyr_asd - last year Joost Schouppe suggested comparing the node density to population density since there is obviously some correlation between those two, i wonder if anyone did something like that since then - this could be used to find the most overmapped/undermapped parts of the world relative to population.

Nice.

This approach however has one big issue - the data is designed for a certain zoom level and the legend does not work when you zoom away from it. For generating a legend this way for all zoom levels you would either need multiple instances of the data at different scales or cut together the legend from different pieces of the rendered map.

zoom levels: Yes, ideally all levels of course but practically adding z12 and z11 would already be good.

imagery offsets:

Most extreme case i remember was in the Lyngen Alps - around here: osm.org/#map=11/69.8068/20.1802

Differences with the same image source can be found here: osm.org/#map=13/62.5597/8.1576

Here i get ~50m difference even at sea level: osm.org/#map=13/70.1720/22.2797

I know these are pretty nasty areas due to steep relief but i generally would expect at least no larger differences with the same image basis (i.e. the same viewing direction) unless you changed the relief data basis. Offset at sea level to me also indicates insufficient quality relief data.

metadata:

Sorry i read over that part. Looking forward to it.

By the way i forgot to mention: Good to see you were able to keep the terms of use fairly plain and simple so you can actually read them without getting a headache.

This is great news, thanks to DG and supporters for making this possible.

A quick look at the imagery indicates there is quite a lot of useful stuff in there - even if there is obviously a lot of overlap with imagery we already know from Bing and Mapbox there are also images were existing sources provide nothing of comparable resolution and furthermore there are many areas where having an additional, different image is useful for verification.

Two quick observations from looking over the new layers at a few places:

• There seem to be quite severe alignment differences between the two layers and DG images from Bing and Mapbox, sometimes even with clearly the same image as basis but apparently processed differently, occasionally several hundred meters in magnitude - like for example in some parts of Norway.
• The starting zoom level for the tiles with the images is fairly late (z13), especially for high latitudes. This makes using the images for finding gaps in mapping like looking for missing islands, lakes etc. quite difficult since you have to zoom in to get the image but then cannot see a larger area any more. So as a suggestion - if you could extend the tiles by a few zoom levels downwards that would be very useful. The imagery from Mapbox you have for z<13 and the Landsat Geocover fallback imagery is not really of use for mapping, it could even be preferable not to deliver tiles without DG images so the editor shows the image layer below instead.
• There is currently no recording date metadata available - this would be extremely helpful for mapping. Bing has this and we have been bugging Mapbox to add it for years with no success.
• Having coverage polygons indicating the image coverage would be great for image source selection as well of course.

According to http://wiki.osmfoundation.org/wiki/Membership/Statistics there were 396 normal members in November 2016 so if there were 485 in early January that is almost frightening.

Wow, that means membership increased by nearly 100 members in half a year.

I got the data from OSMF with the premise not to redistribute it and not to be too detailed when displaying it on a map so that single members cannot be inferred.

I don’t think that was really more successful with you blurred style than it would have been with a more objective display method. All the feint isolated dots are clearly individual members. In your map you don’t really get a good idea how many more members there are in Europe than in the US. It could easily be anywhere between three times and 30 times as many.

Maybe you could give us member numbers per country for the US, Canada and Europe.

Thanks for the effort but providing the underlying data would probably be more useful.

Side note: it never ceases to amaze me how people think that blurring data somehow improves the value of an illustration. A simple dot density or proportional symbol map would be much less distorting. And of course an equal area map projection is generally considered a hard requirement for this kind of illustration as well.

Well - customarily we in OSM do not give much about authoritative classifications - see also https://en.wikipedia.org/wiki/Country and the ambiguities listed there. The HDYC country list is a fairly consistent list of territories (although some of the boundaries used are off leading to quite a bit of stuff being classified as unknown).

This is a fairly abstract exercise of course but you missed at least Greenland, Svalbard and Antarctica (assuming here of course you use the HDYC definition of ‘country’).

Nice image.

Airplanes in imagery are a fairly common sight especially around large and much frequented airports of course. They tell you quite a lot of thing on how the image was taken. The one you showed was for example photographed by a satellite with a higher resolution panchromatic band and significantly lower resolution multispectral sensor with a linear resolution ratio of about 1:4 as typical for today’s commercial high resolution satellites. This manifests in the sharp but colorless plane and several slightly offset blurred images of it in different colors.

For comparison here a typical image of a plane in lower resolution imagery without a separate panchromatic band:

http://maps.imagico.de/#map=13/54.292/8.434&lang=en&l=sat&r=osmim&o=2&ui=8

I understand the worries about spam but you should not blame the admins for that. They are all volunteers. If fighting spam requires additional resources (either in development or operations) you should not automatically assume this has to be provided by the admin team.

Also keep in mind as an outsider you usually only see the spam that is not dealt with while spam that is already dealt with is invisible to you. You do not know for example how many user accounts created by spammers are deleted. If you subscribe to the RSS feed for these diaries you will get to see quite a bit of diary entry spam but most of this is removed and not visible on the website after a short time.

This is standard comment spam - everyone running a website with comment option is usually familiar with this problem.

Relevant links:

osm.wiki/Spam/Report_user

https://github.com/openstreetmap/openstreetmap-website/issues/841

https://github.com/openstreetmap/openstreetmap-website/issues/1083

Another short update: There has now been a user block put on the involved accounts by the DWG:

osm.org/user_blocks/1319

Editing activities in the meantime had continued mostly unchanged after the previous update (which is more selective than earlier but still often questionable regarding the whole approach). The Maproulette challenge is about half complete now with about half of the tasks done being marked as false positive.

I always wonder how these numbers were going to change if you’d exclude imports. Obviously most imports these days are performed through JOSM so it seems likely that the dominance of JOSM in terms of edits as well as the average changeset size of JOSM edits would drop significantly if you’d only look at normal edits. It would be interesting to know by how much though.

With just one data point in 2016 it is not really possible to say much about the effects of Maps.me based on this probably.

@SK53 - yes, illumination differences are one of the biggest problems when doing such analysis. On Tierra del Fuego the mentioned data set has a lot of gaps (obviously considering the prevalence of clouds) and overestimates tree cover - the Hermite Islands for example are depicted with at least about 30-40 percent tree cover.

A few notes on this data:

• this data is not really new - research work this is based on is from 2013/2014 and the data was published more than a year ago IIRC.
• this is not in any way suitable for import in OSM as is although you could consider deriving data from it that could be imported - which however is not a trivial task if you want good results.
• data quality of this is fairly good considering the scope but not great. The methodology how they identify forest is complex and not fully documented. The difficulty here is to identify forests and differentiating them from other types of vegetation. Especially on a global level where you are dealing with a huge variety of ecosystems all with different spectral characteristics this is really hard. In principle this kind of data set usually depicts woody vegetation in general rather than forests/woods in a strict sense. Also note this is not meant as a data source for cartographic purposes but as a basis for detecting and analyzing changes in forest cover.

That being said if a local community is looking for a way to map forests in their area and considers importing or automated processes producing forest polygons using this data could - when done well - lead to more useful results better suitable for subsequent refinement and improvement by hand in OSM than data sources like Corine Land Cover which are inherently unsuited for OSM. None the less you should also keep in mind that locally you can usually do much better if you specifically identify forests on up-to-date open data imagery - either by hand or using automated processes because

1. you can use local knowledge
2. you would have a more recent and higher quality data basis.
3. you can tune your forest detection specifically for the local situation.